Physiotherapy Can Help Recover Functional Status in Community-dwelling Seniors Assessed in Emergency
Bibliographic record
Abstract
Background: Around 75% of seniors seeking treatment for injuries in Emergency Departments (ED) are discharged home with minor injuries that put them at risk of functional decline in the following months. Objectives: To 1) describe seniors’ characteristics using or not physiotherapy services following ED visits for minor injuries and 2) examine their functional status according to physiotherapy use. Methods: Secondary data analyses of the Canadian Emergency Team Initiative cohort study. Participants were 65 years and older, discharged home after consulting EDs for minor injuries and assessed three times: ED, 3- and 6-months. Physiotherapy use was recorded as yes/no. Functional status was measured using the Older American Resources Scale (OARS). Multivariate linear regressions were used to examine change in OARS scores over time, accounting for confounders. Results: Among the 2169 participants, 565 (26%) received physiotherapy, and 1604 (74%) did not. Physiotherapy users were more likely females (71% vs. 64%), more educated, and less cognitively impaired. The overall change in OARS at 6 months was -0.31/28 points (95% CI: -0.55; -0.28) with no difference across groups after adjustment. Subgroup analyses among frail seniors showed that physiotherapy users maintained their function while non-users lost clinically significant function (-0.02 vs. -1.26/28 points, p = 0.03). Among the severely injured (Injury Severity Scale ≥ 5), physiotherapy users’ results were higher by almost 1/28 points (p = 0.03) compared to non-users. Conclusion: These results suggest that among seniors discharged home after consulting the ED for minor injuries, the frail and severely injured may benefit from being systematically referred to physiotherapy services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".